Predict Bike Sharing Demand with AutoGluon Template¶

Project: Predict Bike Sharing Demand with AutoGluon¶

This notebook is a template with each step that you need to complete for the project.

Please fill in your code where there are explicit ? markers in the notebook. You are welcome to add more cells and code as you see fit.

Once you have completed all the code implementations, please export your notebook as a HTML file so the reviews can view your code. Make sure you have all outputs correctly outputted.

File-> Export Notebook As... -> Export Notebook as HTML

There is a writeup to complete as well after all code implememtation is done. Please answer all questions and attach the necessary tables and charts. You can complete the writeup in either markdown or PDF.

Completing the code template and writeup template will cover all of the rubric points for this project.

The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this notebook and also discuss the results in the writeup file.

Step 1: Create an account with Kaggle¶

Create Kaggle Account and download API key¶

Below is example of steps to get the API username and key. Each student will have their own username and key.

  1. Open account settings. kaggle1.png kaggle2.png
  2. Scroll down to API and click Create New API Token. kaggle3.png kaggle4.png
  3. Open up kaggle.json and use the username and key. kaggle5.png

Step 2: Download the Kaggle dataset using the kaggle python library¶

Open up Sagemaker Studio and use starter template¶

  1. Notebook should be using a ml.t3.medium instance (2 vCPU + 4 GiB)
  2. Notebook should be using kernal: Python 3 (MXNet 1.8 Python 3.7 CPU Optimized)

Install packages¶

In [3]:
!pip install -U pip
!pip install -U setuptools wheel
!pip install -U "mxnet<2.0.0" bokeh==2.0.1
!pip install autogluon --no-cache-dir
# Without --no-cache-dir, smaller aws instances may have trouble installing
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Building wheels for collected packages: fairscale, antlr4-python3-runtime, seqeval, future
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Successfully built fairscale antlr4-python3-runtime seqeval future
Installing collected packages: typish, tokenizers, text-unidecode, tensorboard-plugin-wit, sortedcontainers, sentencepiece, py4j, msgpack, heapdict, distlib, cymem, antlr4-python3-runtime, zict, yacs, xxhash, wrapt, typing-extensions, tqdm, toolz, tensorboard-data-server, tblib, spacy-loggers, spacy-legacy, smart-open, regex, pyrsistent, pyDeprecate, pyasn1-modules, Pillow, ordered-set, omegaconf, oauthlib, numpy, murmurhash, multidict, mdurl, locket, langcodes, importlib-resources, grpcio, future, frozenlist, filelock, fastprogress, defusedxml, charset-normalizer, cachetools, autocfg, asynctest, absl-py, yarl, wasabi, torch, tifffile, tensorboardX, scipy, responses, requests-oauthlib, PyWavelets, pydantic, pyarrow, preshed, platformdirs, patsy, partd, opencv-python-headless, nptyping, markdown-it-py, importlib-metadata, google-auth, fastcore, deprecated, catalogue, blis, async-timeout, aiosignal, xgboost, virtualenv, torchvision, torchtext, torchmetrics, statsmodels, srsly, scikit-image, rich, nlpaug, markdown, jsonschema, hyperopt, huggingface-hub, google-auth-oauthlib, gluonts, fastdownload, fairscale, dask, click, aiohttp, accelerate, typer, transformers, timm, tensorboard, sktime, seqeval, ray, qudida, pytorch-metric-learning, pmdarima, nltk, model-index, lightgbm, gluoncv, distributed, confection, catboost, thinc, tbats, pytorch-lightning, pathy, openmim, datasets, autogluon.common, albumentations, spacy, evaluate, autogluon.features, autogluon.core, fastai, autogluon.tabular, autogluon.multimodal, autogluon.vision, autogluon.timeseries, autogluon.text, autogluon
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Successfully installed Pillow-9.4.0 PyWavelets-1.3.0 absl-py-1.4.0 accelerate-0.13.2 aiohttp-3.8.4 aiosignal-1.3.1 albumentations-1.1.0 antlr4-python3-runtime-4.8 async-timeout-4.0.2 asynctest-0.13.0 autocfg-0.0.8 autogluon-0.6.2 autogluon.common-0.6.2 autogluon.core-0.6.2 autogluon.features-0.6.2 autogluon.multimodal-0.6.2 autogluon.tabular-0.6.2 autogluon.text-0.6.2 autogluon.timeseries-0.6.2 autogluon.vision-0.6.2 blis-0.7.9 cachetools-5.3.1 catalogue-2.0.8 catboost-1.1.1 charset-normalizer-3.1.0 click-8.0.4 confection-0.0.4 cymem-2.0.7 dask-2021.11.2 datasets-2.12.0 defusedxml-0.7.1 deprecated-1.2.14 distlib-0.3.6 distributed-2021.11.2 evaluate-0.3.0 fairscale-0.4.6 fastai-2.7.12 fastcore-1.5.29 fastdownload-0.0.7 fastprogress-1.0.3 filelock-3.12.0 frozenlist-1.3.3 future-0.18.3 gluoncv-0.10.5.post0 gluonts-0.11.12 google-auth-2.19.0 google-auth-oauthlib-0.4.6 grpcio-1.43.0 heapdict-1.0.1 huggingface-hub-0.14.1 hyperopt-0.2.7 importlib-metadata-6.6.0 importlib-resources-5.12.0 jsonschema-4.8.0 langcodes-3.3.0 lightgbm-3.3.5 locket-1.0.0 markdown-3.4.3 markdown-it-py-2.2.0 mdurl-0.1.2 model-index-0.1.11 msgpack-1.0.5 multidict-6.0.4 murmurhash-1.0.9 nlpaug-1.1.10 nltk-3.8.1 nptyping-1.4.4 numpy-1.21.6 oauthlib-3.2.2 omegaconf-2.1.2 opencv-python-headless-4.7.0.72 openmim-0.2.1 ordered-set-4.1.0 partd-1.4.0 pathy-0.10.1 patsy-0.5.3 platformdirs-3.1.1 pmdarima-1.8.5 preshed-3.0.8 py4j-0.10.9.7 pyDeprecate-0.3.2 pyarrow-12.0.0 pyasn1-modules-0.3.0 pydantic-1.10.8 pyrsistent-0.19.3 pytorch-lightning-1.7.7 pytorch-metric-learning-1.3.2 qudida-0.0.4 ray-2.0.1 regex-2023.5.5 requests-oauthlib-1.3.1 responses-0.18.0 rich-13.3.5 scikit-image-0.19.3 scipy-1.7.3 sentencepiece-0.1.99 seqeval-1.2.2 sktime-0.13.4 smart-open-5.2.1 sortedcontainers-2.4.0 spacy-3.5.3 spacy-legacy-3.0.12 spacy-loggers-1.0.4 srsly-2.4.6 statsmodels-0.13.5 tbats-1.1.3 tblib-1.7.0 tensorboard-2.11.2 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorboardX-2.6 text-unidecode-1.3 thinc-8.1.10 tifffile-2021.11.2 timm-0.6.13 tokenizers-0.13.3 toolz-0.12.0 torch-1.12.1 torchmetrics-0.8.2 torchtext-0.13.1 torchvision-0.13.1 tqdm-4.65.0 transformers-4.23.1 typer-0.7.0 typing-extensions-4.4.0 typish-1.9.3 virtualenv-20.21.1 wasabi-1.1.1 wrapt-1.15.0 xgboost-1.6.2 xxhash-3.2.0 yacs-0.1.8 yarl-1.9.2 zict-2.2.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

Setup Kaggle API Key¶

In [4]:
# create the .kaggle directory and an empty kaggle.json file
!mkdir -p /root/.kaggle
!touch /root/.kaggle/kaggle.json
!chmod 600 /root/.kaggle/kaggle.json
In [5]:
# Fill in your user name and key from creating the kaggle account and API token file
import json
kaggle_username = "kr1shn6"
kaggle_key = "30facf08f77e16221c2a269a0242866f"

# Save API token the kaggle.json file
with open("/root/.kaggle/kaggle.json", "w") as f:
    f.write(json.dumps({"username": kaggle_username, "key": kaggle_key}))
In [8]:
!pip install kaggle
Collecting kaggle
  Downloading kaggle-1.5.13.tar.gz (63 kB)
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Building wheels for collected packages: kaggle
  Building wheel for kaggle (setup.py) ... done
  Created wheel for kaggle: filename=kaggle-1.5.13-py3-none-any.whl size=77717 sha256=521de080f786a2bb5f1a4f4fc489edd98e85203907e29d8f9935946e57a7ccb9
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Successfully built kaggle
Installing collected packages: python-slugify, kaggle
Successfully installed kaggle-1.5.13 python-slugify-8.0.1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

Download and explore dataset¶

Go to the bike sharing demand competition and agree to the terms¶

kaggle6.png

In [11]:
# Download the dataset, it will be in a .zip file so you'll need to unzip it as well.
!kaggle competitions download -c bike-sharing-demand
# If you already downloaded it you can use the -o command to overwrite the file
!unzip -o bike-sharing-demand.zip
Downloading bike-sharing-demand.zip to /root/cd0385-project-starter/project
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100%|████████████████████████████████████████| 189k/189k [00:00<00:00, 7.93MB/s]
Archive:  bike-sharing-demand.zip
  inflating: sampleSubmission.csv    
  inflating: test.csv                
  inflating: train.csv               
In [13]:
import pandas as pd
from autogluon.tabular import TabularPredictor
In [14]:
# Run this cell to import or install the Data Wrangler widget to show automatic visualization and generate code to fix data quality issues

try:
    import sagemaker_datawrangler
except ImportError:
    !pip install --upgrade sagemaker-datawrangler
    import sagemaker_datawrangler

# Display Pandas DataFrame to view the widget: df, display(df), df.sample()... 
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WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
In [19]:
# Create the train dataset in pandas by reading the csv
# Set the parsing of the datetime column so you can use some of the `dt` features in pandas later
train = pd.read_csv("CSV Files/train.csv",parse_dates=["datetime"])
train.head()
             datetime  season  holiday  workingday  weather  temp   atemp  \
0 2011-01-01 00:00:00       1        0           0        1  9.84  14.395   
1 2011-01-01 01:00:00       1        0           0        1  9.02  13.635   
2 2011-01-01 02:00:00       1        0           0        1  9.02  13.635   
3 2011-01-01 03:00:00       1        0           0        1  9.84  14.395   
4 2011-01-01 04:00:00       1        0           0        1  9.84  14.395   

   humidity  windspeed  casual  registered  count  
0        81        0.0       3          13     16  
1        80        0.0       8          32     40  
2        80        0.0       5          27     32  
3        75        0.0       3          10     13  
4        75        0.0       0           1      1  
In [20]:
# Simple output of the train dataset to view some of the min/max/varition of the dataset features.
train.describe()
             season       holiday    workingday       weather         temp  \
count  10886.000000  10886.000000  10886.000000  10886.000000  10886.00000   
mean       2.506614      0.028569      0.680875      1.418427     20.23086   
std        1.116174      0.166599      0.466159      0.633839      7.79159   
min        1.000000      0.000000      0.000000      1.000000      0.82000   
25%        2.000000      0.000000      0.000000      1.000000     13.94000   
50%        3.000000      0.000000      1.000000      1.000000     20.50000   
75%        4.000000      0.000000      1.000000      2.000000     26.24000   
max        4.000000      1.000000      1.000000      4.000000     41.00000   

              atemp      humidity     windspeed        casual    registered  \
count  10886.000000  10886.000000  10886.000000  10886.000000  10886.000000   
mean      23.655084     61.886460     12.799395     36.021955    155.552177   
std        8.474601     19.245033      8.164537     49.960477    151.039033   
min        0.760000      0.000000      0.000000      0.000000      0.000000   
25%       16.665000     47.000000      7.001500      4.000000     36.000000   
50%       24.240000     62.000000     12.998000     17.000000    118.000000   
75%       31.060000     77.000000     16.997900     49.000000    222.000000   
max       45.455000    100.000000     56.996900    367.000000    886.000000   

              count  
count  10886.000000  
mean     191.574132  
std      181.144454  
min        1.000000  
25%       42.000000  
50%      145.000000  
75%      284.000000  
max      977.000000  
In [21]:
# Create the test pandas dataframe in pandas by reading the csv, remember to parse the datetime!
test = pd.read_csv("CSV Files/test.csv",parse_dates=["datetime"])
test.head()
             datetime  season  holiday  workingday  weather   temp   atemp  \
0 2011-01-20 00:00:00       1        0           1        1  10.66  11.365   
1 2011-01-20 01:00:00       1        0           1        1  10.66  13.635   
2 2011-01-20 02:00:00       1        0           1        1  10.66  13.635   
3 2011-01-20 03:00:00       1        0           1        1  10.66  12.880   
4 2011-01-20 04:00:00       1        0           1        1  10.66  12.880   

   humidity  windspeed  
0        56    26.0027  
1        56     0.0000  
2        56     0.0000  
3        56    11.0014  
4        56    11.0014  
In [22]:
# Same thing as train and test dataset
submission = pd.read_csv("CSV Files/sampleSubmission.csv",parse_dates=["datetime"])
submission.head()
             datetime  count
0 2011-01-20 00:00:00      0
1 2011-01-20 01:00:00      0
2 2011-01-20 02:00:00      0
3 2011-01-20 03:00:00      0
4 2011-01-20 04:00:00      0

Step 3: Train a model using AutoGluon’s Tabular Prediction¶

Requirements:

  • We are prediting count, so it is the label we are setting.
  • Ignore casual and registered columns as they are also not present in the test dataset.
  • Use the root_mean_squared_error as the metric to use for evaluation.
  • Set a time limit of 10 minutes (600 seconds).
  • Use the preset best_quality to focus on creating the best model.
In [25]:
train_data = train

# Define the label column
label = 'count'


# Define the evaluation metric
eval_metric = 'root_mean_squared_error'

# Set the time limit
time_limit = 600

# Set the preset
preset = 'best_quality'

# Create the predictor
predictor = TabularPredictor(label=label, eval_metric=eval_metric, 
                             learner_kwargs={"ignored_columns": ["casual", "registered"]}).fit(
    train_data=train_data, 
    time_limit=time_limit, 
    presets=preset
)
No path specified. Models will be saved in: "AutogluonModels/ag-20230528_210642/"
Presets specified: ['best_quality']
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20230528_210642/"
AutoGluon Version:  0.6.2
Python Version:     3.7.10
Operating System:   Linux
Platform Machine:   x86_64
Platform Version:   #1 SMP Tue Apr 25 15:24:19 UTC 2023
Train Data Rows:    10886
Train Data Columns: 11
Label Column: count
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).
	Label info (max, min, mean, stddev): (977, 1, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Dropping user-specified ignored columns: ['casual', 'registered']
Fitting AutoMLPipelineFeatureGenerator...
	Available Memory:                    3005.77 MB
	Train Data (Original)  Memory Usage: 0.78 MB (0.0% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
			Note: Converting 2 features to boolean dtype as they only contain 2 unique values.
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Types of features in original data (raw dtype, special dtypes):
		('datetime', []) : 1 | ['datetime']
		('float', [])    : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])      : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']
	Types of features in processed data (raw dtype, special dtypes):
		('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])                  : 3 | ['season', 'weather', 'humidity']
		('int', ['bool'])            : 2 | ['holiday', 'workingday']
		('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
	0.3s = Fit runtime
	9 features in original data used to generate 13 features in processed data.
	Train Data (Processed) Memory Usage: 0.98 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.33s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
	To change this, specify the eval_metric parameter of Predictor()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.68s of the 599.67s of remaining time.
	-101.5462	 = Validation score   (-root_mean_squared_error)
	0.03s	 = Training   runtime
	0.1s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 395.47s of the 595.46s of remaining time.
	-84.1251	 = Validation score   (-root_mean_squared_error)
	0.03s	 = Training   runtime
	0.1s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 395.11s of the 595.1s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-131.4609	 = Validation score   (-root_mean_squared_error)
	67.85s	 = Training   runtime
	7.8s	 = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 315.87s of the 515.85s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-131.0542	 = Validation score   (-root_mean_squared_error)
	39.05s	 = Training   runtime
	1.81s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 270.12s of the 470.11s of remaining time.
	-116.5443	 = Validation score   (-root_mean_squared_error)
	11.9s	 = Training   runtime
	0.54s	 = Validation runtime
Fitting model: CatBoost_BAG_L1 ... Training model for up to 254.83s of the 454.82s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-130.5332	 = Validation score   (-root_mean_squared_error)
	191.55s	 = Training   runtime
	0.11s	 = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 59.5s of the 259.49s of remaining time.
	-124.5881	 = Validation score   (-root_mean_squared_error)
	4.95s	 = Training   runtime
	0.53s	 = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 51.37s of the 251.36s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-138.8018	 = Validation score   (-root_mean_squared_error)
	67.76s	 = Training   runtime
	0.42s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 178.82s of remaining time.
	-84.1251	 = Validation score   (-root_mean_squared_error)
	0.48s	 = Training   runtime
	0.0s	 = Validation runtime
Fitting 9 L2 models ...
Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 178.27s of the 178.25s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-60.2107	 = Validation score   (-root_mean_squared_error)
	53.14s	 = Training   runtime
	3.04s	 = Validation runtime
Fitting model: LightGBM_BAG_L2 ... Training model for up to 119.98s of the 119.96s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-55.1772	 = Validation score   (-root_mean_squared_error)
	25.94s	 = Training   runtime
	0.24s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 89.91s of the 89.9s of remaining time.
	-53.4113	 = Validation score   (-root_mean_squared_error)
	26.51s	 = Training   runtime
	0.6s	 = Validation runtime
Fitting model: CatBoost_BAG_L2 ... Training model for up to 60.34s of the 60.32s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-55.573	 = Validation score   (-root_mean_squared_error)
	60.63s	 = Training   runtime
	0.06s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the -4.1s of remaining time.
	-53.0998	 = Validation score   (-root_mean_squared_error)
	0.31s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 604.59s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20230528_210642/")

Review AutoGluon's training run with ranking of models that did the best.¶

In [26]:
predictor.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
                     model   score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0      WeightedEnsemble_L3  -53.099799      15.355100  549.653708                0.000796           0.313764            3       True         14
1   RandomForestMSE_BAG_L2  -53.411253      12.014673  409.634342                0.596428          26.507615            2       True         12
2          LightGBM_BAG_L2  -55.177207      11.662676  409.062004                0.244431          25.935277            2       True         11
3          CatBoost_BAG_L2  -55.572998      11.474988  443.760034                0.056743          60.633307            2       True         13
4        LightGBMXT_BAG_L2  -60.210742      14.456702  436.263745                3.038457          53.137018            2       True         10
5    KNeighborsDist_BAG_L1  -84.125061       0.103669    0.029130                0.103669           0.029130            1       True          2
6      WeightedEnsemble_L2  -84.125061       0.104435    0.511747                0.000766           0.482617            2       True          9
7    KNeighborsUnif_BAG_L1 -101.546199       0.103915    0.031923                0.103915           0.031923            1       True          1
8   RandomForestMSE_BAG_L1 -116.544294       0.543514   11.901437                0.543514          11.901437            1       True          5
9     ExtraTreesMSE_BAG_L1 -124.588053       0.528831    4.947814                0.528831           4.947814            1       True          7
10         CatBoost_BAG_L1 -130.533194       0.105419  191.554991                0.105419         191.554991            1       True          6
11         LightGBM_BAG_L1 -131.054162       1.812739   39.047134                1.812739          39.047134            1       True          4
12       LightGBMXT_BAG_L1 -131.460909       7.802163   67.849963                7.802163          67.849963            1       True          3
13  NeuralNetFastAI_BAG_L1 -138.801849       0.417995   67.764336                0.417995          67.764336            1       True          8
Number of models trained: 14
Types of models trained:
{'StackerEnsembleModel_LGB', 'WeightedEnsembleModel', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_XT'}
Bagging used: True  (with 8 folds)
Multi-layer stack-ensembling used: True  (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
('int', [])                  : 3 | ['season', 'weather', 'humidity']
('int', ['bool'])            : 2 | ['holiday', 'workingday']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
Plot summary of models saved to file: AutogluonModels/ag-20230528_210642/SummaryOfModels.html
*** End of fit() summary ***
Out[26]:
{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
  'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',
  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
  'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',
  'WeightedEnsemble_L2': 'WeightedEnsembleModel',
  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
  'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',
  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
 'model_performance': {'KNeighborsUnif_BAG_L1': -101.54619908446061,
  'KNeighborsDist_BAG_L1': -84.12506123181602,
  'LightGBMXT_BAG_L1': -131.46090891834504,
  'LightGBM_BAG_L1': -131.054161598899,
  'RandomForestMSE_BAG_L1': -116.54429428704391,
  'CatBoost_BAG_L1': -130.5331939673838,
  'ExtraTreesMSE_BAG_L1': -124.58805258915959,
  'NeuralNetFastAI_BAG_L1': -138.80184878939485,
  'WeightedEnsemble_L2': -84.12506123181602,
  'LightGBMXT_BAG_L2': -60.21074168177454,
  'LightGBM_BAG_L2': -55.17720686751347,
  'RandomForestMSE_BAG_L2': -53.41125302253621,
  'CatBoost_BAG_L2': -55.5729984932244,
  'WeightedEnsemble_L3': -53.0997991005159},
 'model_best': 'WeightedEnsemble_L3',
 'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels/ag-20230528_210642/models/KNeighborsUnif_BAG_L1/',
  'KNeighborsDist_BAG_L1': 'AutogluonModels/ag-20230528_210642/models/KNeighborsDist_BAG_L1/',
  'LightGBMXT_BAG_L1': 'AutogluonModels/ag-20230528_210642/models/LightGBMXT_BAG_L1/',
  'LightGBM_BAG_L1': 'AutogluonModels/ag-20230528_210642/models/LightGBM_BAG_L1/',
  'RandomForestMSE_BAG_L1': 'AutogluonModels/ag-20230528_210642/models/RandomForestMSE_BAG_L1/',
  'CatBoost_BAG_L1': 'AutogluonModels/ag-20230528_210642/models/CatBoost_BAG_L1/',
  'ExtraTreesMSE_BAG_L1': 'AutogluonModels/ag-20230528_210642/models/ExtraTreesMSE_BAG_L1/',
  'NeuralNetFastAI_BAG_L1': 'AutogluonModels/ag-20230528_210642/models/NeuralNetFastAI_BAG_L1/',
  'WeightedEnsemble_L2': 'AutogluonModels/ag-20230528_210642/models/WeightedEnsemble_L2/',
  'LightGBMXT_BAG_L2': 'AutogluonModels/ag-20230528_210642/models/LightGBMXT_BAG_L2/',
  'LightGBM_BAG_L2': 'AutogluonModels/ag-20230528_210642/models/LightGBM_BAG_L2/',
  'RandomForestMSE_BAG_L2': 'AutogluonModels/ag-20230528_210642/models/RandomForestMSE_BAG_L2/',
  'CatBoost_BAG_L2': 'AutogluonModels/ag-20230528_210642/models/CatBoost_BAG_L2/',
  'WeightedEnsemble_L3': 'AutogluonModels/ag-20230528_210642/models/WeightedEnsemble_L3/'},
 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.03192257881164551,
  'KNeighborsDist_BAG_L1': 0.029129505157470703,
  'LightGBMXT_BAG_L1': 67.84996318817139,
  'LightGBM_BAG_L1': 39.047133684158325,
  'RandomForestMSE_BAG_L1': 11.901436805725098,
  'CatBoost_BAG_L1': 191.55499148368835,
  'ExtraTreesMSE_BAG_L1': 4.9478137493133545,
  'NeuralNetFastAI_BAG_L1': 67.76433610916138,
  'WeightedEnsemble_L2': 0.4826173782348633,
  'LightGBMXT_BAG_L2': 53.13701796531677,
  'LightGBM_BAG_L2': 25.935276746749878,
  'RandomForestMSE_BAG_L2': 26.507615089416504,
  'CatBoost_BAG_L2': 60.63330674171448,
  'WeightedEnsemble_L3': 0.3137643337249756},
 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.10391521453857422,
  'KNeighborsDist_BAG_L1': 0.1036689281463623,
  'LightGBMXT_BAG_L1': 7.802163124084473,
  'LightGBM_BAG_L1': 1.812739372253418,
  'RandomForestMSE_BAG_L1': 0.5435137748718262,
  'CatBoost_BAG_L1': 0.10541868209838867,
  'ExtraTreesMSE_BAG_L1': 0.5288305282592773,
  'NeuralNetFastAI_BAG_L1': 0.4179952144622803,
  'WeightedEnsemble_L2': 0.0007660388946533203,
  'LightGBMXT_BAG_L2': 3.038456678390503,
  'LightGBM_BAG_L2': 0.2444312572479248,
  'RandomForestMSE_BAG_L2': 0.5964279174804688,
  'CatBoost_BAG_L2': 0.05674266815185547,
  'WeightedEnsemble_L3': 0.0007963180541992188},
 'num_bag_folds': 8,
 'max_stack_level': 3,
 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'KNeighborsDist_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'LightGBMXT_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'CatBoost_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'NeuralNetFastAI_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L2': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBMXT_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'CatBoost_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L3': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True}},
 'leaderboard':                      model   score_val  pred_time_val    fit_time  \
 0      WeightedEnsemble_L3  -53.099799      15.355100  549.653708   
 1   RandomForestMSE_BAG_L2  -53.411253      12.014673  409.634342   
 2          LightGBM_BAG_L2  -55.177207      11.662676  409.062004   
 3          CatBoost_BAG_L2  -55.572998      11.474988  443.760034   
 4        LightGBMXT_BAG_L2  -60.210742      14.456702  436.263745   
 5    KNeighborsDist_BAG_L1  -84.125061       0.103669    0.029130   
 6      WeightedEnsemble_L2  -84.125061       0.104435    0.511747   
 7    KNeighborsUnif_BAG_L1 -101.546199       0.103915    0.031923   
 8   RandomForestMSE_BAG_L1 -116.544294       0.543514   11.901437   
 9     ExtraTreesMSE_BAG_L1 -124.588053       0.528831    4.947814   
 10         CatBoost_BAG_L1 -130.533194       0.105419  191.554991   
 11         LightGBM_BAG_L1 -131.054162       1.812739   39.047134   
 12       LightGBMXT_BAG_L1 -131.460909       7.802163   67.849963   
 13  NeuralNetFastAI_BAG_L1 -138.801849       0.417995   67.764336   
 
     pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
 0                 0.000796           0.313764            3       True   
 1                 0.596428          26.507615            2       True   
 2                 0.244431          25.935277            2       True   
 3                 0.056743          60.633307            2       True   
 4                 3.038457          53.137018            2       True   
 5                 0.103669           0.029130            1       True   
 6                 0.000766           0.482617            2       True   
 7                 0.103915           0.031923            1       True   
 8                 0.543514          11.901437            1       True   
 9                 0.528831           4.947814            1       True   
 10                0.105419         191.554991            1       True   
 11                1.812739          39.047134            1       True   
 12                7.802163          67.849963            1       True   
 13                0.417995          67.764336            1       True   
 
     fit_order  
 0          14  
 1          12  
 2          11  
 3          13  
 4          10  
 5           2  
 6           9  
 7           1  
 8           5  
 9           7  
 10          6  
 11          4  
 12          3  
 13          8  }

Create predictions from test dataset¶

In [27]:
predictions = predictor.predict(test)
predictions.head()
Out[27]:
0    23.758947
1    41.277710
2    45.014797
3    48.974281
4    51.911652
Name: count, dtype: float32
In [28]:
predictions.describe()
Out[28]:
count    6493.000000
mean      101.020172
std        90.073212
min         3.378276
25%        20.027142
50%        64.318069
75%       167.423874
max       363.106232
Name: count, dtype: float64

NOTE: Kaggle will reject the submission if we don't set everything to be > 0.¶

In [30]:
# Describe the `predictions` series to see if there are any negative values
# How many negative values do we have?
# Set them to zero
predictions[predictions<0] = 0

Set predictions to submission dataframe, save, and submit¶

In [31]:
submission["count"] = predictions
submission.to_csv("submission.csv", index=False)
In [32]:
!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m "first raw submission"
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 411kB/s]
Successfully submitted to Bike Sharing Demand

View submission via the command line or in the web browser under the competition's page - My Submissions¶

In [33]:
!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName        date                 description           status    publicScore  privateScore  
--------------  -------------------  --------------------  --------  -----------  ------------  
submission.csv  2023-05-28 21:23:03  first raw submission  complete  1.78979      1.78979       

Initial score of 1.789¶

Step 4: Exploratory Data Analysis and Creating an additional feature¶

  • Any additional feature will do, but a great suggestion would be to separate out the datetime into hour, day, or month parts.
In [34]:
# Create a histogram of all features to show the distribution of each one relative to the data. This is part of the exploritory data analysis
train.hist(figsize=(20,20))
Out[34]:
array([[<AxesSubplot:title={'center':'datetime'}>,
        <AxesSubplot:title={'center':'season'}>,
        <AxesSubplot:title={'center':'holiday'}>],
       [<AxesSubplot:title={'center':'workingday'}>,
        <AxesSubplot:title={'center':'weather'}>,
        <AxesSubplot:title={'center':'temp'}>],
       [<AxesSubplot:title={'center':'atemp'}>,
        <AxesSubplot:title={'center':'humidity'}>,
        <AxesSubplot:title={'center':'windspeed'}>],
       [<AxesSubplot:title={'center':'casual'}>,
        <AxesSubplot:title={'center':'registered'}>,
        <AxesSubplot:title={'center':'count'}>]], dtype=object)
In [35]:
# Extracting 'hour', 'dayofweek', 'month', and 'year' from 'datetime' and creating new features
train['hour'] = train['datetime'].dt.hour
train['dayofweek'] = train['datetime'].dt.dayofweek
train['month'] = train['datetime'].dt.month
train['year'] = train['datetime'].dt.year

test['hour'] = test['datetime'].dt.hour
test['dayofweek'] = test['datetime'].dt.dayofweek
test['month'] = test['datetime'].dt.month
test['year'] = test['datetime'].dt.year
In [36]:
# Create a histogram of all features
train.hist(figsize=(20,20))
Out[36]:
array([[<AxesSubplot:title={'center':'datetime'}>,
        <AxesSubplot:title={'center':'season'}>,
        <AxesSubplot:title={'center':'holiday'}>,
        <AxesSubplot:title={'center':'workingday'}>],
       [<AxesSubplot:title={'center':'weather'}>,
        <AxesSubplot:title={'center':'temp'}>,
        <AxesSubplot:title={'center':'atemp'}>,
        <AxesSubplot:title={'center':'humidity'}>],
       [<AxesSubplot:title={'center':'windspeed'}>,
        <AxesSubplot:title={'center':'casual'}>,
        <AxesSubplot:title={'center':'registered'}>,
        <AxesSubplot:title={'center':'count'}>],
       [<AxesSubplot:title={'center':'hour'}>,
        <AxesSubplot:title={'center':'dayofweek'}>,
        <AxesSubplot:title={'center':'month'}>,
        <AxesSubplot:title={'center':'year'}>]], dtype=object)

Make category types for these so models know they are not just numbers¶

  • AutoGluon originally sees these as ints, but in reality they are int representations of a category.
  • Setting the dtype to category will classify these as categories in AutoGluon.
In [37]:
# Make 'season' and 'weather' features as categorical
train["season"] = train["season"].astype('category')
train["weather"] = train["weather"].astype('category')

test["season"] = test["season"].astype('category')
test["weather"] = test["weather"].astype('category')
In [38]:
# View are new feature
train.head()
             datetime season  holiday  workingday weather  temp   atemp  \
0 2011-01-01 00:00:00      1        0           0       1  9.84  14.395   
1 2011-01-01 01:00:00      1        0           0       1  9.02  13.635   
2 2011-01-01 02:00:00      1        0           0       1  9.02  13.635   
3 2011-01-01 03:00:00      1        0           0       1  9.84  14.395   
4 2011-01-01 04:00:00      1        0           0       1  9.84  14.395   

   humidity  windspeed  casual  registered  count  hour  dayofweek  month  \
0        81        0.0       3          13     16     0          5      1   
1        80        0.0       8          32     40     1          5      1   
2        80        0.0       5          27     32     2          5      1   
3        75        0.0       3          10     13     3          5      1   
4        75        0.0       0           1      1     4          5      1   

   year  
0  2011  
1  2011  
2  2011  
3  2011  
4  2011  
In [39]:
# View histogram of all features again now with the hour feature
train.hist(figsize=(20,20))
Out[39]:
array([[<AxesSubplot:title={'center':'datetime'}>,
        <AxesSubplot:title={'center':'holiday'}>,
        <AxesSubplot:title={'center':'workingday'}>,
        <AxesSubplot:title={'center':'temp'}>],
       [<AxesSubplot:title={'center':'atemp'}>,
        <AxesSubplot:title={'center':'humidity'}>,
        <AxesSubplot:title={'center':'windspeed'}>,
        <AxesSubplot:title={'center':'casual'}>],
       [<AxesSubplot:title={'center':'registered'}>,
        <AxesSubplot:title={'center':'count'}>,
        <AxesSubplot:title={'center':'hour'}>,
        <AxesSubplot:title={'center':'dayofweek'}>],
       [<AxesSubplot:title={'center':'month'}>,
        <AxesSubplot:title={'center':'year'}>, <AxesSubplot:>,
        <AxesSubplot:>]], dtype=object)

Additional Feature Creation¶

Extracting additional time-related features such as day of the month, week of the year, quarter of the year.

In [42]:
train['dayofmonth'] = train['datetime'].dt.day
train['weekofyear'] = train['datetime'].dt.isocalendar().week
train['quarter'] = train['datetime'].dt.quarter

test['dayofmonth'] = test['datetime'].dt.day
test['weekofyear'] = test['datetime'].dt.isocalendar().week
test['quarter'] = test['datetime'].dt.quarter
In [43]:
train.head()
             datetime season  holiday  workingday weather  temp   atemp  \
0 2011-01-01 00:00:00      1        0           0       1  9.84  14.395   
1 2011-01-01 01:00:00      1        0           0       1  9.02  13.635   
2 2011-01-01 02:00:00      1        0           0       1  9.02  13.635   
3 2011-01-01 03:00:00      1        0           0       1  9.84  14.395   
4 2011-01-01 04:00:00      1        0           0       1  9.84  14.395   

   humidity  windspeed  casual  registered  count  hour  dayofweek  month  \
0        81        0.0       3          13     16     0          5      1   
1        80        0.0       8          32     40     1          5      1   
2        80        0.0       5          27     32     2          5      1   
3        75        0.0       3          10     13     3          5      1   
4        75        0.0       0           1      1     4          5      1   

   year  dayofmonth  weekofyear  quarter  
0  2011           1          52        1  
1  2011           1          52        1  
2  2011           1          52        1  
3  2011           1          52        1  
4  2011           1          52        1  

Correlation Matrix Heat Map:¶

In [45]:
import seaborn as sns
import matplotlib.pyplot as plt
corr = train.corr()
plt.figure(figsize=(15,10))
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.show()

Analysis from Correlation Heat Map:¶

From the correlation matrix, we can observe the following key insights:

  1. The "temp" and "atemp" features are highly correlated (0.984948), indicating a strong relationship between actual temperature and perceived temperature.

  2. Bike sharing demand, represented by the "count" feature, shows positive correlations with "temp" (0.394454) and "atemp" (0.389784), suggesting that higher temperatures are associated with increased bike usage.

  3. The "hour" feature has a positive correlation (0.400601) with the bike sharing demand, indicating that certain hours of the day have higher demand for bikes.

  4. The "workingday" feature has a negative correlation (-0.704267) with the "dayofweek" feature, indicating that weekdays are typically associated with working days, while weekends have a higher likelihood of being non-working days or holidays.

  5. The "month" feature exhibits a positive correlation (0.257589) with bike sharing demand, suggesting that bike usage varies across different months.

  6. The "year" feature has a positive correlation (0.260403) with bike sharing demand, indicating that bike usage has increased over the years.

These insights highlight the influence of weather conditions, time of day, and calendar factors on bike sharing demand. They can help in understanding the key drivers of bike usage and inform strategies for optimizing bike sharing systems.

Time Series of Bike-Sharing Demand¶

In [46]:
plt.figure(figsize=(15,10))
plt.plot(train['datetime'], train['count'])
plt.title('Time Series of Bike-Sharing Demand')
plt.xlabel('Date')
plt.ylabel('Count')
plt.show()

Time Series of Bike-Sharing Demand¶

The Time Series of Bike-Sharing Demand plot showcases the trend of bike-sharing demand over time. Here are the key observations:

  • The plot reveals a clear increasing trend in bike-sharing demand over the years, indicating a growing popularity of bike sharing as a mode of transportation.
  • There are noticeable seasonal patterns in bike-sharing demand, with peaks during certain months or periods of the year. This suggests that factors such as weather and holidays influence the usage of bike-sharing services.
  • The plot also shows fluctuations in demand within each year, indicating variations in bike-sharing usage across different seasons or months.

Understanding the time series patterns of bike-sharing demand can help in identifying the factors that drive the demand and can aid in making informed decisions related to resource allocation, marketing strategies, and service optimization.

Pairplot for Multivariate Analysis:¶

In [50]:
sns.pairplot(train)
plt.show()

sns.pairplot Analysis¶

The sns.pairplot function provides a visual representation of the relationships between pairs of features in the Bike-Sharing Demand dataset. Here are the key observations from the pairplot:

  • The distribution of the "count" feature shows a positive skew, indicating that bike-sharing demand is generally higher during certain periods.
  • There is a strong positive correlation between the "count" feature and the "temp" and "atemp" features, suggesting that higher temperatures are associated with increased bike usage.
  • The "count" feature also exhibits a positive correlation with the "hour" feature, indicating that certain hours of the day experience higher demand for bike sharing.
  • The "count" feature shows a negative correlation with the "humidity" feature, suggesting that higher humidity levels are associated with lower bike-sharing demand.
  • The "count" feature appears to have a positive correlation with the "month" and "year" features, indicating seasonal and yearly variations in bike-sharing demand.

Overall, the pairplot provides valuable insights into the relationships between features and their impact on bike-sharing demand.

Step 5: Rerun the model with the same settings as before, just with more features¶

In [55]:
train = train.drop(columns=['casual', 'registered'])

# Define the TabularPredictor object
predictor_new_features = TabularPredictor(
    label="count",
    problem_type="regression",
    eval_metric="root_mean_squared_error"
)

# Fit the model
predictor_new_features.fit(train_data=train, time_limit=600, presets="best_quality")

predictor_new_features.fit_summary()
No path specified. Models will be saved in: "AutogluonModels/ag-20230528_220520/"
Presets specified: ['best_quality']
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20230528_220520/"
AutoGluon Version:  0.6.2
Python Version:     3.7.10
Operating System:   Linux
Platform Machine:   x86_64
Platform Version:   #1 SMP Tue Apr 25 15:24:19 UTC 2023
Train Data Rows:    10886
Train Data Columns: 16
Label Column: count
Preprocessing data ...
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
	Available Memory:                    1549.01 MB
	Train Data (Original)  Memory Usage: 1.21 MB (0.1% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
			Note: Converting 3 features to boolean dtype as they only contain 2 unique values.
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Unused Original Features (Count: 1): ['weekofyear']
		These features were not used to generate any of the output features. Add a feature generator compatible with these features to utilize them.
		Features can also be unused if they carry very little information, such as being categorical but having almost entirely unique values or being duplicates of other features.
		These features do not need to be present at inference time.
		('UInt32', []) : 1 | ['weekofyear']
	Types of features in original data (raw dtype, special dtypes):
		('category', []) : 2 | ['season', 'weather']
		('datetime', []) : 1 | ['datetime']
		('float', [])    : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])      : 9 | ['holiday', 'workingday', 'humidity', 'hour', 'dayofweek', ...]
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             : 2 | ['season', 'weather']
		('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])                  : 6 | ['humidity', 'hour', 'dayofweek', 'month', 'dayofmonth', ...]
		('int', ['bool'])            : 3 | ['holiday', 'workingday', 'year']
		('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
	0.3s = Fit runtime
	15 features in original data used to generate 19 features in processed data.
	Train Data (Processed) Memory Usage: 1.27 MB (0.1% of available memory)
Data preprocessing and feature engineering runtime = 0.35s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
	To change this, specify the eval_metric parameter of Predictor()
AutoGluon will fit 2 stack levels (L1 to L2) ...
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 399.67s of the 599.64s of remaining time.
	-101.5462	 = Validation score   (-root_mean_squared_error)
	0.1s	 = Training   runtime
	0.2s	 = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 399.11s of the 599.09s of remaining time.
	-84.1251	 = Validation score   (-root_mean_squared_error)
	0.04s	 = Training   runtime
	0.1s	 = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 398.74s of the 598.72s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-34.3862	 = Validation score   (-root_mean_squared_error)
	88.7s	 = Training   runtime
	8.63s	 = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 301.54s of the 501.52s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-33.9173	 = Validation score   (-root_mean_squared_error)
	46.57s	 = Training   runtime
	3.04s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L1 ... Training model for up to 249.43s of the 449.4s of remaining time.
	-38.3838	 = Validation score   (-root_mean_squared_error)
	16.12s	 = Training   runtime
	0.58s	 = Validation runtime
Fitting model: CatBoost_BAG_L1 ... Training model for up to 230.35s of the 430.33s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-33.9994	 = Validation score   (-root_mean_squared_error)
	199.02s	 = Training   runtime
	0.18s	 = Validation runtime
Fitting model: ExtraTreesMSE_BAG_L1 ... Training model for up to 27.28s of the 227.25s of remaining time.
	-37.8255	 = Validation score   (-root_mean_squared_error)
	7.08s	 = Training   runtime
	0.56s	 = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 17.07s of the 217.04s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-112.3675	 = Validation score   (-root_mean_squared_error)
	42.42s	 = Training   runtime
	0.47s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 170.52s of remaining time.
	-32.1571	 = Validation score   (-root_mean_squared_error)
	0.57s	 = Training   runtime
	0.0s	 = Validation runtime
Fitting 9 L2 models ...
Fitting model: LightGBMXT_BAG_L2 ... Training model for up to 169.87s of the 169.85s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-31.2317	 = Validation score   (-root_mean_squared_error)
	31.76s	 = Training   runtime
	0.78s	 = Validation runtime
Fitting model: LightGBM_BAG_L2 ... Training model for up to 133.14s of the 133.11s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-30.6498	 = Validation score   (-root_mean_squared_error)
	26.52s	 = Training   runtime
	0.4s	 = Validation runtime
Fitting model: RandomForestMSE_BAG_L2 ... Training model for up to 102.5s of the 102.48s of remaining time.
	-31.5285	 = Validation score   (-root_mean_squared_error)
	34.17s	 = Training   runtime
	0.62s	 = Validation runtime
Fitting model: CatBoost_BAG_L2 ... Training model for up to 65.4s of the 65.37s of remaining time.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	-30.6801	 = Validation score   (-root_mean_squared_error)
	64.96s	 = Training   runtime
	0.11s	 = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the -3.5s of remaining time.
	-30.3484	 = Validation score   (-root_mean_squared_error)
	0.32s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 604.01s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20230528_220520/")
*** Summary of fit() ***
Estimated performance of each model:
                     model   score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0      WeightedEnsemble_L3  -30.348431      15.681876  557.767127                0.000849           0.315693            3       True         14
1          LightGBM_BAG_L2  -30.649798      14.173486  426.556313                0.395302          26.515152            2       True         11
2          CatBoost_BAG_L2  -30.680087      13.883778  464.998940                0.105594          64.957778            2       True         13
3        LightGBMXT_BAG_L2  -31.231659      14.556585  431.803588                0.778400          31.762426            2       True         10
4   RandomForestMSE_BAG_L2  -31.528455      14.401731  434.216078                0.623547          34.174917            2       True         12
5      WeightedEnsemble_L2  -32.157071      12.540704  351.006814                0.000880           0.571393            2       True          9
6          LightGBM_BAG_L1  -33.917339       3.042789   46.567111                3.042789          46.567111            1       True          4
7          CatBoost_BAG_L1  -33.999386       0.182528  199.016673                0.182528         199.016673            1       True          6
8        LightGBMXT_BAG_L1  -34.386233       8.626113   88.695049                8.626113          88.695049            1       True          3
9     ExtraTreesMSE_BAG_L1  -37.825541       0.561626    7.083585                0.561626           7.083585            1       True          7
10  RandomForestMSE_BAG_L1  -38.383826       0.584332   16.119021                0.584332          16.119021            1       True          5
11   KNeighborsDist_BAG_L1  -84.125061       0.104061    0.037567                0.104061           0.037567            1       True          2
12   KNeighborsUnif_BAG_L1 -101.546199       0.204905    0.099342                0.204905           0.099342            1       True          1
13  NeuralNetFastAI_BAG_L1 -112.367509       0.471830   42.422813                0.471830          42.422813            1       True          8
Number of models trained: 14
Types of models trained:
{'StackerEnsembleModel_LGB', 'WeightedEnsembleModel', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_XT'}
Bagging used: True  (with 8 folds)
Multi-layer stack-ensembling used: True  (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', [])             : 2 | ['season', 'weather']
('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
('int', [])                  : 6 | ['humidity', 'hour', 'dayofweek', 'month', 'dayofmonth', ...]
('int', ['bool'])            : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
Plot summary of models saved to file: AutogluonModels/ag-20230528_220520/SummaryOfModels.html
*** End of fit() summary ***
Out[55]:
{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',
  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',
  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L1': 'StackerEnsembleModel_RF',
  'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',
  'ExtraTreesMSE_BAG_L1': 'StackerEnsembleModel_XT',
  'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',
  'WeightedEnsemble_L2': 'WeightedEnsembleModel',
  'LightGBMXT_BAG_L2': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2': 'StackerEnsembleModel_LGB',
  'RandomForestMSE_BAG_L2': 'StackerEnsembleModel_RF',
  'CatBoost_BAG_L2': 'StackerEnsembleModel_CatBoost',
  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
 'model_performance': {'KNeighborsUnif_BAG_L1': -101.54619908446061,
  'KNeighborsDist_BAG_L1': -84.12506123181602,
  'LightGBMXT_BAG_L1': -34.38623285339942,
  'LightGBM_BAG_L1': -33.91733862651761,
  'RandomForestMSE_BAG_L1': -38.383825608046195,
  'CatBoost_BAG_L1': -33.999386109312795,
  'ExtraTreesMSE_BAG_L1': -37.825540568014624,
  'NeuralNetFastAI_BAG_L1': -112.3675088612664,
  'WeightedEnsemble_L2': -32.15707078392907,
  'LightGBMXT_BAG_L2': -31.23165925218751,
  'LightGBM_BAG_L2': -30.649798213137323,
  'RandomForestMSE_BAG_L2': -31.528454612704277,
  'CatBoost_BAG_L2': -30.680086877468266,
  'WeightedEnsemble_L3': -30.348430830311507},
 'model_best': 'WeightedEnsemble_L3',
 'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels/ag-20230528_220520/models/KNeighborsUnif_BAG_L1/',
  'KNeighborsDist_BAG_L1': 'AutogluonModels/ag-20230528_220520/models/KNeighborsDist_BAG_L1/',
  'LightGBMXT_BAG_L1': 'AutogluonModels/ag-20230528_220520/models/LightGBMXT_BAG_L1/',
  'LightGBM_BAG_L1': 'AutogluonModels/ag-20230528_220520/models/LightGBM_BAG_L1/',
  'RandomForestMSE_BAG_L1': 'AutogluonModels/ag-20230528_220520/models/RandomForestMSE_BAG_L1/',
  'CatBoost_BAG_L1': 'AutogluonModels/ag-20230528_220520/models/CatBoost_BAG_L1/',
  'ExtraTreesMSE_BAG_L1': 'AutogluonModels/ag-20230528_220520/models/ExtraTreesMSE_BAG_L1/',
  'NeuralNetFastAI_BAG_L1': 'AutogluonModels/ag-20230528_220520/models/NeuralNetFastAI_BAG_L1/',
  'WeightedEnsemble_L2': 'AutogluonModels/ag-20230528_220520/models/WeightedEnsemble_L2/',
  'LightGBMXT_BAG_L2': 'AutogluonModels/ag-20230528_220520/models/LightGBMXT_BAG_L2/',
  'LightGBM_BAG_L2': 'AutogluonModels/ag-20230528_220520/models/LightGBM_BAG_L2/',
  'RandomForestMSE_BAG_L2': 'AutogluonModels/ag-20230528_220520/models/RandomForestMSE_BAG_L2/',
  'CatBoost_BAG_L2': 'AutogluonModels/ag-20230528_220520/models/CatBoost_BAG_L2/',
  'WeightedEnsemble_L3': 'AutogluonModels/ag-20230528_220520/models/WeightedEnsemble_L3/'},
 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.09934234619140625,
  'KNeighborsDist_BAG_L1': 0.0375666618347168,
  'LightGBMXT_BAG_L1': 88.69504880905151,
  'LightGBM_BAG_L1': 46.5671112537384,
  'RandomForestMSE_BAG_L1': 16.11902141571045,
  'CatBoost_BAG_L1': 199.01667284965515,
  'ExtraTreesMSE_BAG_L1': 7.083585023880005,
  'NeuralNetFastAI_BAG_L1': 42.422813177108765,
  'WeightedEnsemble_L2': 0.5713932514190674,
  'LightGBMXT_BAG_L2': 31.762426137924194,
  'LightGBM_BAG_L2': 26.515151739120483,
  'RandomForestMSE_BAG_L2': 34.17491674423218,
  'CatBoost_BAG_L2': 64.95777821540833,
  'WeightedEnsemble_L3': 0.315692663192749},
 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.20490455627441406,
  'KNeighborsDist_BAG_L1': 0.10406136512756348,
  'LightGBMXT_BAG_L1': 8.626113414764404,
  'LightGBM_BAG_L1': 3.0427889823913574,
  'RandomForestMSE_BAG_L1': 0.5843319892883301,
  'CatBoost_BAG_L1': 0.1825275421142578,
  'ExtraTreesMSE_BAG_L1': 0.5616259574890137,
  'NeuralNetFastAI_BAG_L1': 0.4718303680419922,
  'WeightedEnsemble_L2': 0.0008802413940429688,
  'LightGBMXT_BAG_L2': 0.7784004211425781,
  'LightGBM_BAG_L2': 0.39530205726623535,
  'RandomForestMSE_BAG_L2': 0.623546838760376,
  'CatBoost_BAG_L2': 0.10559368133544922,
  'WeightedEnsemble_L3': 0.0008492469787597656},
 'num_bag_folds': 8,
 'max_stack_level': 3,
 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'KNeighborsDist_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'LightGBMXT_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'CatBoost_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'ExtraTreesMSE_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'NeuralNetFastAI_BAG_L1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L2': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBMXT_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'RandomForestMSE_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True,
   'use_child_oof': True},
  'CatBoost_BAG_L2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L3': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True}},
 'leaderboard':                      model   score_val  pred_time_val    fit_time  \
 0      WeightedEnsemble_L3  -30.348431      15.681876  557.767127   
 1          LightGBM_BAG_L2  -30.649798      14.173486  426.556313   
 2          CatBoost_BAG_L2  -30.680087      13.883778  464.998940   
 3        LightGBMXT_BAG_L2  -31.231659      14.556585  431.803588   
 4   RandomForestMSE_BAG_L2  -31.528455      14.401731  434.216078   
 5      WeightedEnsemble_L2  -32.157071      12.540704  351.006814   
 6          LightGBM_BAG_L1  -33.917339       3.042789   46.567111   
 7          CatBoost_BAG_L1  -33.999386       0.182528  199.016673   
 8        LightGBMXT_BAG_L1  -34.386233       8.626113   88.695049   
 9     ExtraTreesMSE_BAG_L1  -37.825541       0.561626    7.083585   
 10  RandomForestMSE_BAG_L1  -38.383826       0.584332   16.119021   
 11   KNeighborsDist_BAG_L1  -84.125061       0.104061    0.037567   
 12   KNeighborsUnif_BAG_L1 -101.546199       0.204905    0.099342   
 13  NeuralNetFastAI_BAG_L1 -112.367509       0.471830   42.422813   
 
     pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
 0                 0.000849           0.315693            3       True   
 1                 0.395302          26.515152            2       True   
 2                 0.105594          64.957778            2       True   
 3                 0.778400          31.762426            2       True   
 4                 0.623547          34.174917            2       True   
 5                 0.000880           0.571393            2       True   
 6                 3.042789          46.567111            1       True   
 7                 0.182528         199.016673            1       True   
 8                 8.626113          88.695049            1       True   
 9                 0.561626           7.083585            1       True   
 10                0.584332          16.119021            1       True   
 11                0.104061           0.037567            1       True   
 12                0.204905           0.099342            1       True   
 13                0.471830          42.422813            1       True   
 
     fit_order  
 0          14  
 1          11  
 2          13  
 3          10  
 4          12  
 5           9  
 6           4  
 7           6  
 8           3  
 9           7  
 10          5  
 11          2  
 12          1  
 13          8  }
In [56]:
predictions_new_features = predictor_new_features.predict(test)
predictions_new_features.head()
Out[56]:
0    15.499533
1    11.199882
2    10.766752
3     9.099400
4     8.308702
Name: count, dtype: float32
In [57]:
# Remember to set all negative values to zero
predictions_new_features[predictions_new_features < 0] = 0
In [60]:
# Same submitting predictions
submission_new_features = pd.read_csv("CSV Files/sampleSubmission.csv", parse_dates=["datetime"])
submission_new_features["count"] = predictions_new_features
submission_new_features.to_csv("submission_new_features.csv", index=False)
In [61]:
!kaggle competitions submit -c bike-sharing-demand -f CSV Files/submission_new_features.csv -m "new features"
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 488kB/s]
Successfully submitted to Bike Sharing Demand
In [62]:
!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName                     date                 description           status    publicScore  privateScore  
---------------------------  -------------------  --------------------  --------  -----------  ------------  
submission_new_features.csv  2023-05-28 22:17:19  new features          complete  0.66081      0.66081       
submission.csv               2023-05-28 21:23:03  first raw submission  complete  1.78979      1.78979       

New Score of 0.66081¶

Step 6: Hyper parameter optimization¶

  • There are many options for hyper parameter optimization.
  • Options are to change the AutoGluon higher level parameters or the individual model hyperparameters.
  • The hyperparameters of the models themselves that are in AutoGluon. Those need the hyperparameter and hyperparameter_tune_kwargs arguments.
In [80]:
import autogluon.core as ag

nn_options = {  # Specifies non-default hyperparameter values for neural network models
    'num_epochs': 10,  'dropout_prob': ag.space.Real(0.0, 0.5, default=0.1),  'layers': ag.space.Categorical([100], [1000], [200, 100], [300, 200, 100]), 'learning_rate': ag.space.Real(1e-4, 1e-2, default=5e-4, log=True), 'activation': ag.space.Categorical('relu', 'softrelu', 'tanh')
}

gbm_options = {   #hyperparameter - lightGBM gradient boosted trees
  'num_leaves': ag.space.Int(lower=26, upper=66, default=36), 'num_boost_round': 100,      
}

hyperparameters = {'GBM': gbm_options, 'NN': nn_options}

num_trials = 10  # Try at most 50 different hyperparameter configurations for each type of model
search_strategy = 'auto'  

hyperparameter_tune_kwargs = {  
    'num_trials': num_trials,
    'scheduler' : 'local',
    'searcher': search_strategy,
}
In [81]:
predictor_new_hpo = TabularPredictor(label="count", eval_metric="root_mean_squared_error").fit(
    train_data=train, 
    time_limit=600, 
    presets="best_quality", 
    hyperparameters=hyperparameters, 
    hyperparameter_tune_kwargs=hyperparameter_tune_kwargs,
)
No path specified. Models will be saved in: "AutogluonModels/ag-20230528_225927/"
Presets specified: ['best_quality']
Warning: hyperparameter tuning is currently experimental and may cause the process to hang.
Stack configuration (auto_stack=True): num_stack_levels=1, num_bag_folds=8, num_bag_sets=20
Beginning AutoGluon training ... Time limit = 600s
AutoGluon will save models to "AutogluonModels/ag-20230528_225927/"
AutoGluon Version:  0.6.2
Python Version:     3.7.10
Operating System:   Linux
Platform Machine:   x86_64
Platform Version:   #1 SMP Tue Apr 25 15:24:19 UTC 2023
Train Data Rows:    10886
Train Data Columns: 16
Label Column: count
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).
	Label info (max, min, mean, stddev): (977, 1, 191.57413, 181.14445)
	If 'regression' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
	Available Memory:                    1488.34 MB
	Train Data (Original)  Memory Usage: 1.21 MB (0.1% of available memory)
	Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
	Stage 1 Generators:
		Fitting AsTypeFeatureGenerator...
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
			Note: Converting 3 features to boolean dtype as they only contain 2 unique values.
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
	Stage 2 Generators:
		Fitting FillNaFeatureGenerator...
Warning: dtype UInt32 is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...
Cannot interpret 'UInt32Dtype()' as a data type
	Stage 3 Generators:
		Fitting IdentityFeatureGenerator...
		Fitting CategoryFeatureGenerator...
			Fitting CategoryMemoryMinimizeFeatureGenerator...
		Fitting DatetimeFeatureGenerator...
	Stage 4 Generators:
		Fitting DropUniqueFeatureGenerator...
	Unused Original Features (Count: 1): ['weekofyear']
		These features were not used to generate any of the output features. Add a feature generator compatible with these features to utilize them.
		Features can also be unused if they carry very little information, such as being categorical but having almost entirely unique values or being duplicates of other features.
		These features do not need to be present at inference time.
		('UInt32', []) : 1 | ['weekofyear']
	Types of features in original data (raw dtype, special dtypes):
		('category', []) : 2 | ['season', 'weather']
		('datetime', []) : 1 | ['datetime']
		('float', [])    : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])      : 9 | ['holiday', 'workingday', 'humidity', 'hour', 'dayofweek', ...]
	Types of features in processed data (raw dtype, special dtypes):
		('category', [])             : 2 | ['season', 'weather']
		('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
		('int', [])                  : 6 | ['humidity', 'hour', 'dayofweek', 'month', 'dayofmonth', ...]
		('int', ['bool'])            : 3 | ['holiday', 'workingday', 'year']
		('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
	0.2s = Fit runtime
	15 features in original data used to generate 19 features in processed data.
	Train Data (Processed) Memory Usage: 1.27 MB (0.1% of available memory)
Data preprocessing and feature engineering runtime = 0.33s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
	This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
	To change this, specify the eval_metric parameter of Predictor()
AutoGluon will fit 2 stack levels (L1 to L2) ...
	WARNING: "NN" model has been deprecated in v0.4.0 and renamed to "NN_MXNET". Starting in v0.6.0, specifying "NN" or "NN_MXNET" will raise an exception. Consider instead specifying "NN_TORCH".
Fitting 2 L1 models ...
Hyperparameter tuning model: LightGBM_BAG_L1 ... Tuning model for up to 179.86s of the 599.67s of remaining time.
  0%|          | 0/10 [00:00<?, ?it/s]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
 10%|█         | 1/10 [00:22<03:23, 22.63s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
 20%|██        | 2/10 [00:52<03:34, 26.85s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
 30%|███       | 3/10 [01:16<02:58, 25.47s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
 40%|████      | 4/10 [01:40<02:29, 24.97s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
 50%|█████     | 5/10 [02:03<02:02, 24.42s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
 60%|██████    | 6/10 [02:27<01:36, 24.13s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	Stopping HPO to satisfy time limit...
 60%|██████    | 6/10 [02:51<01:54, 28.50s/it]
Fitted model: LightGBM_BAG_L1/T1 ...
	-40.2554	 = Validation score   (-root_mean_squared_error)
	22.59s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T2 ...
	-39.1125	 = Validation score   (-root_mean_squared_error)
	29.77s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T3 ...
	-38.4725	 = Validation score   (-root_mean_squared_error)
	23.8s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T4 ...
	-121.8294	 = Validation score   (-root_mean_squared_error)
	24.17s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T5 ...
	-43.1759	 = Validation score   (-root_mean_squared_error)
	23.43s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T6 ...
	-109.5652	 = Validation score   (-root_mean_squared_error)
	23.51s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L1/T7 ...
	-38.3638	 = Validation score   (-root_mean_squared_error)
	23.49s	 = Training   runtime
	0.0s	 = Validation runtime
Hyperparameter tuning model: NeuralNetMXNet_BAG_L1 ... Tuning model for up to 179.86s of the 428.5s of remaining time.
  0%|          | 0/10 [00:00<?, ?it/s]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=24706, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=24706, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 10%|█         | 1/10 [00:05<00:53,  5.99s/it]2023-05-28 23:02:25,000	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=24766, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=24766, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 20%|██        | 2/10 [00:11<00:45,  5.67s/it]2023-05-28 23:02:30,446	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=24829, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=24829, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 30%|███       | 3/10 [00:17<00:39,  5.65s/it]2023-05-28 23:02:36,026	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=24832, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=24893, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=24893, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 40%|████      | 4/10 [00:23<00:35,  5.92s/it]2023-05-28 23:02:42,366	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=24992, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=24992, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 50%|█████     | 5/10 [00:28<00:28,  5.79s/it]2023-05-28 23:02:47,948	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=25057, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=25057, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 60%|██████    | 6/10 [00:34<00:22,  5.71s/it]2023-05-28 23:02:53,491	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=25122, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=25122, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 70%|███████   | 7/10 [00:40<00:16,  5.67s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
2023-05-28 23:02:59,671	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
ray::_ray_fit() (pid=25188, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=25188, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 80%|████████  | 8/10 [00:46<00:11,  5.87s/it]2023-05-28 23:03:05,409	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=25282, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=25282, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 90%|█████████ | 9/10 [00:51<00:05,  5.73s/it]2023-05-28 23:03:10,870	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=25346, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=25346, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
100%|██████████| 10/10 [00:58<00:00,  5.80s/it]
No model was trained during hyperparameter tuning NeuralNetMXNet_BAG_L1... Skipping this model.
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 370.32s of remaining time.
2023-05-28 23:03:17,065	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	-37.6393	 = Validation score   (-root_mean_squared_error)
	0.57s	 = Training   runtime
	0.0s	 = Validation runtime
	WARNING: "NN" model has been deprecated in v0.4.0 and renamed to "NN_MXNET". Starting in v0.6.0, specifying "NN" or "NN_MXNET" will raise an exception. Consider instead specifying "NN_TORCH".
Fitting 2 L2 models ...
Hyperparameter tuning model: LightGBM_BAG_L2 ... Tuning model for up to 166.34s of the 369.62s of remaining time.
  0%|          | 0/10 [00:00<?, ?it/s]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
 10%|█         | 1/10 [00:22<03:25, 22.85s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
 20%|██        | 2/10 [00:46<03:06, 23.27s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
 30%|███       | 3/10 [01:10<02:46, 23.85s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
 40%|████      | 4/10 [01:34<02:22, 23.80s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
 50%|█████     | 5/10 [02:00<02:02, 24.56s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
	Stopping HPO to satisfy time limit...
 50%|█████     | 5/10 [02:24<02:24, 28.97s/it]
Fitted model: LightGBM_BAG_L2/T1 ...
	-36.6163	 = Validation score   (-root_mean_squared_error)
	22.82s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T2 ...
	-36.4008	 = Validation score   (-root_mean_squared_error)
	23.53s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T3 ...
	-36.6577	 = Validation score   (-root_mean_squared_error)
	24.5s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T4 ...
	-102.82	 = Validation score   (-root_mean_squared_error)
	23.7s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T5 ...
	-37.1291	 = Validation score   (-root_mean_squared_error)
	25.87s	 = Training   runtime
	0.0s	 = Validation runtime
Fitted model: LightGBM_BAG_L2/T6 ...
	-99.7662	 = Validation score   (-root_mean_squared_error)
	24.26s	 = Training   runtime
	0.0s	 = Validation runtime
Hyperparameter tuning model: NeuralNetMXNet_BAG_L2 ... Tuning model for up to 166.34s of the 224.55s of remaining time.
  0%|          | 0/10 [00:00<?, ?it/s]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=26876, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=26876, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 10%|█         | 1/10 [00:05<00:52,  5.80s/it]2023-05-28 23:05:48,788	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=26939, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=26939, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 20%|██        | 2/10 [00:11<00:45,  5.66s/it]2023-05-28 23:05:54,313	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=26936, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=27005, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=27005, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 30%|███       | 3/10 [00:17<00:40,  5.72s/it]2023-05-28 23:06:00,155	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): ray::_ray_fit() (pid=27002, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=27075, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=27075, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 40%|████      | 4/10 [00:24<00:38,  6.47s/it]2023-05-28 23:06:07,763	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=27139, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=27139, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 50%|█████     | 5/10 [00:30<00:30,  6.12s/it]	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
2023-05-28 23:06:13,566	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
ray::_ray_fit() (pid=27205, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=27205, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 60%|██████    | 6/10 [00:36<00:24,  6.05s/it]2023-05-28 23:06:19,168	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=27302, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=27302, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 70%|███████   | 7/10 [00:42<00:18,  6.01s/it]2023-05-28 23:06:25,131	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=27393, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=27393, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 80%|████████  | 8/10 [00:47<00:11,  5.84s/it]2023-05-28 23:06:30,611	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=27459, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=27459, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
 90%|█████████ | 9/10 [00:53<00:05,  5.82s/it]2023-05-28 23:06:36,348	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
	Fitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy
ray::_ray_fit() (pid=27524, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 49, in model_trial
    time_limit=time_limit,
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/model_trial.py", line 101, in fit_and_save_model
    model.fit(**fit_args, time_limit=time_left)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/stacker_ensemble_model.py", line 154, in _fit
    return super()._fit(X=X, y=y, time_limit=time_limit, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 251, in _fit
    n_repeats=n_repeats, n_repeat_start=n_repeat_start, save_folds=save_bag_folds, groups=groups, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/bagged_ensemble_model.py", line 541, in _fit_folds
    fold_fitting_strategy.after_all_folds_scheduled()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 536, in after_all_folds_scheduled
    raise processed_exception
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 504, in after_all_folds_scheduled
    time_end_fit, predict_time, predict_1_time = self.ray.get(finished)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/client_mode_hook.py", line 105, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/ray/_private/worker.py", line 2280, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::_ray_fit() (pid=27524, ip=169.255.255.2)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/ensemble/fold_fitting_strategy.py", line 375, in _ray_fit
    time_limit=time_limit_fold, **resources, **kwargs_fold)
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/models/abstract/abstract_model.py", line 703, in fit
    out = self._fit(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/autogluon/tabular/models/tabular_nn/mxnet/tabular_nn_mxnet.py", line 135, in _fit
    try_import_mxnet()
  File "/usr/local/lib/python3.7/site-packages/autogluon/core/utils/try_import.py", line 40, in try_import_mxnet
    import mxnet as mx
  File "/usr/local/lib/python3.7/site-packages/mxnet/__init__.py", line 33, in <module>
    from . import contrib
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/__init__.py", line 30, in <module>
    from . import text
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/__init__.py", line 23, in <module>
    from . import embedding
  File "/usr/local/lib/python3.7/site-packages/mxnet/contrib/text/embedding.py", line 37, in <module>
    from ... import numpy_extension as _mx_npx
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/__init__.py", line 23, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/numpy_extension/image.py", line 20, in <module>
    from ..image import *  # pylint: disable=wildcard-import, unused-wildcard-import
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/__init__.py", line 22, in <module>
    from . import image
  File "/usr/local/lib/python3.7/site-packages/mxnet/image/image.py", line 38, in <module>
    import cv2
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 181, in <module>
    bootstrap()
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 175, in bootstrap
    if __load_extra_py_code_for_module("cv2", submodule, DEBUG):
  File "/usr/local/lib/python3.7/site-packages/cv2/__init__.py", line 28, in __load_extra_py_code_for_module
    py_module = importlib.import_module(module_name)
  File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "/usr/local/lib/python3.7/site-packages/cv2/gapi/__init__.py", line 301, in <module>
    cv.gapi.wip.GStreamerPipeline = cv.gapi_wip_gst_GStreamerPipeline
AttributeError: module 'cv2' has no attribute 'gapi_wip_gst_GStreamerPipeline'
100%|██████████| 10/10 [00:58<00:00,  5.88s/it]
No model was trained during hyperparameter tuning NeuralNetMXNet_BAG_L2... Skipping this model.
Completed 1/20 k-fold bagging repeats ...
2023-05-28 23:06:41,816	ERROR worker.py:400 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
Fitting model: WeightedEnsemble_L3 ... Training model for up to 360.0s of the 165.54s of remaining time.
	-36.272	 = Validation score   (-root_mean_squared_error)
	0.59s	 = Training   runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 435.27s ... Best model: "WeightedEnsemble_L3"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20230528_225927/")
In [82]:
predictor_new_hpo.fit_summary()
*** Summary of fit() ***
Estimated performance of each model:
                  model   score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0   WeightedEnsemble_L3  -36.271977       0.003667  219.393433                0.001082           0.594797            3       True         15
1    LightGBM_BAG_L2/T2  -36.400794       0.002482  194.296825                0.000094          23.528380            2       True         10
2    LightGBM_BAG_L2/T1  -36.616293       0.002534  193.589968                0.000146          22.821523            2       True          9
3    LightGBM_BAG_L2/T3  -36.657726       0.002491  195.270256                0.000103          24.501811            2       True         11
4    LightGBM_BAG_L2/T5  -37.129110       0.002523  196.635863                0.000135          25.867418            2       True         13
5   WeightedEnsemble_L2  -37.639263       0.001453   47.865143                0.001244           0.568873            2       True          8
6    LightGBM_BAG_L1/T7  -38.363848       0.000104   23.494382                0.000104          23.494382            1       True          7
7    LightGBM_BAG_L1/T3  -38.472461       0.000105   23.801888                0.000105          23.801888            1       True          3
8    LightGBM_BAG_L1/T2  -39.112474       0.000154   29.773199                0.000154          29.773199            1       True          2
9    LightGBM_BAG_L1/T1  -40.255449       0.000103   22.586776                0.000103          22.586776            1       True          1
10   LightGBM_BAG_L1/T5  -43.175926       0.001631   23.425673                0.001631          23.425673            1       True          5
11   LightGBM_BAG_L2/T6  -99.766173       0.002539  195.024213                0.000151          24.255768            2       True         14
12   LightGBM_BAG_L2/T4 -102.819976       0.002516  194.472584                0.000128          23.704139            2       True         12
13   LightGBM_BAG_L1/T6 -109.565161       0.000143   23.514707                0.000143          23.514707            1       True          6
14   LightGBM_BAG_L1/T4 -121.829367       0.000148   24.171821                0.000148          24.171821            1       True          4
Number of models trained: 15
Types of models trained:
{'StackerEnsembleModel_LGB', 'WeightedEnsembleModel'}
Bagging used: True  (with 8 folds)
Multi-layer stack-ensembling used: True  (with 3 levels)
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', [])             : 2 | ['season', 'weather']
('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
('int', [])                  : 6 | ['humidity', 'hour', 'dayofweek', 'month', 'dayofmonth', ...]
('int', ['bool'])            : 3 | ['holiday', 'workingday', 'year']
('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
Plot summary of models saved to file: AutogluonModels/ag-20230528_225927/SummaryOfModels.html
*** End of fit() summary ***
Out[82]:
{'model_types': {'LightGBM_BAG_L1/T1': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T2': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T3': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T4': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T5': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T6': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L1/T7': 'StackerEnsembleModel_LGB',
  'WeightedEnsemble_L2': 'WeightedEnsembleModel',
  'LightGBM_BAG_L2/T1': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T2': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T3': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T4': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T5': 'StackerEnsembleModel_LGB',
  'LightGBM_BAG_L2/T6': 'StackerEnsembleModel_LGB',
  'WeightedEnsemble_L3': 'WeightedEnsembleModel'},
 'model_performance': {'LightGBM_BAG_L1/T1': -40.255448619289915,
  'LightGBM_BAG_L1/T2': -39.11247429212556,
  'LightGBM_BAG_L1/T3': -38.47246129942717,
  'LightGBM_BAG_L1/T4': -121.8293672105454,
  'LightGBM_BAG_L1/T5': -43.175926470419526,
  'LightGBM_BAG_L1/T6': -109.56516071183998,
  'LightGBM_BAG_L1/T7': -38.36384847553418,
  'WeightedEnsemble_L2': -37.63926260672266,
  'LightGBM_BAG_L2/T1': -36.616293007502655,
  'LightGBM_BAG_L2/T2': -36.40079359691757,
  'LightGBM_BAG_L2/T3': -36.65772569539349,
  'LightGBM_BAG_L2/T4': -102.81997585254157,
  'LightGBM_BAG_L2/T5': -37.12910954442744,
  'LightGBM_BAG_L2/T6': -99.76617250885762,
  'WeightedEnsemble_L3': -36.271977458673284},
 'model_best': 'WeightedEnsemble_L3',
 'model_paths': {'LightGBM_BAG_L1/T1': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L1/T1/',
  'LightGBM_BAG_L1/T2': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L1/T2/',
  'LightGBM_BAG_L1/T3': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L1/T3/',
  'LightGBM_BAG_L1/T4': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L1/T4/',
  'LightGBM_BAG_L1/T5': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L1/T5/',
  'LightGBM_BAG_L1/T6': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L1/T6/',
  'LightGBM_BAG_L1/T7': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L1/T7/',
  'WeightedEnsemble_L2': 'AutogluonModels/ag-20230528_225927/models/WeightedEnsemble_L2/',
  'LightGBM_BAG_L2/T1': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L2/T1/',
  'LightGBM_BAG_L2/T2': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L2/T2/',
  'LightGBM_BAG_L2/T3': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L2/T3/',
  'LightGBM_BAG_L2/T4': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L2/T4/',
  'LightGBM_BAG_L2/T5': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L2/T5/',
  'LightGBM_BAG_L2/T6': '/root/cd0385-project-starter/project/AutogluonModels/ag-20230528_225927/models/LightGBM_BAG_L2/T6/',
  'WeightedEnsemble_L3': 'AutogluonModels/ag-20230528_225927/models/WeightedEnsemble_L3/'},
 'model_fit_times': {'LightGBM_BAG_L1/T1': 22.586775541305542,
  'LightGBM_BAG_L1/T2': 29.773199319839478,
  'LightGBM_BAG_L1/T3': 23.80188751220703,
  'LightGBM_BAG_L1/T4': 24.171821355819702,
  'LightGBM_BAG_L1/T5': 23.42567253112793,
  'LightGBM_BAG_L1/T6': 23.5147066116333,
  'LightGBM_BAG_L1/T7': 23.49438214302063,
  'WeightedEnsemble_L2': 0.5688731670379639,
  'LightGBM_BAG_L2/T1': 22.8215229511261,
  'LightGBM_BAG_L2/T2': 23.528379917144775,
  'LightGBM_BAG_L2/T3': 24.501811265945435,
  'LightGBM_BAG_L2/T4': 23.70413875579834,
  'LightGBM_BAG_L2/T5': 25.867417812347412,
  'LightGBM_BAG_L2/T6': 24.255768060684204,
  'WeightedEnsemble_L3': 0.594796895980835},
 'model_pred_times': {'LightGBM_BAG_L1/T1': 0.00010323524475097656,
  'LightGBM_BAG_L1/T2': 0.00015425682067871094,
  'LightGBM_BAG_L1/T3': 0.0001049041748046875,
  'LightGBM_BAG_L1/T4': 0.0001480579376220703,
  'LightGBM_BAG_L1/T5': 0.001630544662475586,
  'LightGBM_BAG_L1/T6': 0.00014281272888183594,
  'LightGBM_BAG_L1/T7': 0.00010418891906738281,
  'WeightedEnsemble_L2': 0.0012438297271728516,
  'LightGBM_BAG_L2/T1': 0.00014591217041015625,
  'LightGBM_BAG_L2/T2': 9.369850158691406e-05,
  'LightGBM_BAG_L2/T3': 0.00010323524475097656,
  'LightGBM_BAG_L2/T4': 0.00012826919555664062,
  'LightGBM_BAG_L2/T5': 0.0001347064971923828,
  'LightGBM_BAG_L2/T6': 0.00015115737915039062,
  'WeightedEnsemble_L3': 0.001081705093383789},
 'num_bag_folds': 8,
 'max_stack_level': 3,
 'model_hyperparams': {'LightGBM_BAG_L1/T1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T3': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T4': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T5': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T6': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L1/T7': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L2': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T1': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T2': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T3': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T4': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T5': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'LightGBM_BAG_L2/T6': {'use_orig_features': True,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True},
  'WeightedEnsemble_L3': {'use_orig_features': False,
   'max_base_models': 25,
   'max_base_models_per_type': 5,
   'save_bag_folds': True}},
 'leaderboard':                   model   score_val  pred_time_val    fit_time  \
 0   WeightedEnsemble_L3  -36.271977       0.003667  219.393433   
 1    LightGBM_BAG_L2/T2  -36.400794       0.002482  194.296825   
 2    LightGBM_BAG_L2/T1  -36.616293       0.002534  193.589968   
 3    LightGBM_BAG_L2/T3  -36.657726       0.002491  195.270256   
 4    LightGBM_BAG_L2/T5  -37.129110       0.002523  196.635863   
 5   WeightedEnsemble_L2  -37.639263       0.001453   47.865143   
 6    LightGBM_BAG_L1/T7  -38.363848       0.000104   23.494382   
 7    LightGBM_BAG_L1/T3  -38.472461       0.000105   23.801888   
 8    LightGBM_BAG_L1/T2  -39.112474       0.000154   29.773199   
 9    LightGBM_BAG_L1/T1  -40.255449       0.000103   22.586776   
 10   LightGBM_BAG_L1/T5  -43.175926       0.001631   23.425673   
 11   LightGBM_BAG_L2/T6  -99.766173       0.002539  195.024213   
 12   LightGBM_BAG_L2/T4 -102.819976       0.002516  194.472584   
 13   LightGBM_BAG_L1/T6 -109.565161       0.000143   23.514707   
 14   LightGBM_BAG_L1/T4 -121.829367       0.000148   24.171821   
 
     pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \
 0                 0.001082           0.594797            3       True   
 1                 0.000094          23.528380            2       True   
 2                 0.000146          22.821523            2       True   
 3                 0.000103          24.501811            2       True   
 4                 0.000135          25.867418            2       True   
 5                 0.001244           0.568873            2       True   
 6                 0.000104          23.494382            1       True   
 7                 0.000105          23.801888            1       True   
 8                 0.000154          29.773199            1       True   
 9                 0.000103          22.586776            1       True   
 10                0.001631          23.425673            1       True   
 11                0.000151          24.255768            2       True   
 12                0.000128          23.704139            2       True   
 13                0.000143          23.514707            1       True   
 14                0.000148          24.171821            1       True   
 
     fit_order  
 0          15  
 1          10  
 2           9  
 3          11  
 4          13  
 5           8  
 6           7  
 7           3  
 8           2  
 9           1  
 10          5  
 11         14  
 12         12  
 13          6  
 14          4  }
In [83]:
# Remember to set all negative values to zero
new_predictions_hpo = predictor_new_hpo.predict(test)
new_predictions_hpo[new_predictions_hpo<0] = 0
In [84]:
# Same submitting predictions
submission_new_hpo = pd.read_csv("CSV Files/sampleSubmission.csv", parse_dates=["datetime"])
submission_new_hpo["count"] = new_predictions_hpo
submission_new_hpo.to_csv("submission_new_hpo.csv", index=False)
In [85]:
!kaggle competitions submit -c bike-sharing-demand -f submission_new_hpo.csv -m "new features with hyperparameters"
100%|█████████████████████████████████████████| 188k/188k [00:00<00:00, 465kB/s]
Successfully submitted to Bike Sharing Demand
In [87]:
!kaggle competitions submissions -c bike-sharing-demand | tail -n +1 | head -n 6
fileName                     date                 description                        status    publicScore  privateScore  
---------------------------  -------------------  ---------------------------------  --------  -----------  ------------  
submission_new_hpo.csv       2023-05-28 23:08:46  new features with hyperparameters  complete  0.47959      0.47959       
submission_new_hpo.csv       2023-05-28 22:47:01  new features with hyperparameters  complete  0.99321      0.99321       
submission_new_features.csv  2023-05-28 22:17:19  new features                       complete  0.66081      0.66081       
submission.csv               2023-05-28 21:23:03  first raw submission               complete  1.78979      1.78979       

New Score of 0.47959¶

Step 7: Write a Report¶

Refer to the markdown file for the full report¶

Creating plots and table for report¶

In [89]:
# Taking the top model score from each training run and creating a line plot to show improvement
# You can create these in the notebook and save them to PNG or use some other tool (e.g. google sheets, excel)
fig = pd.DataFrame(
    {
        "model": ["initial", "add_features", "hpo"],
        "score": [-53.099799, -30.348431, -36.271977 ]
    }
).plot(x="model", y="score", figsize=(8, 6)).get_figure()
fig.savefig('model_train_score.png')
In [90]:
# Take the 3 kaggle scores and creating a line plot to show improvement
fig = pd.DataFrame(
    {
        "test_eval": ["initial", "add_features", "hpo"],
        "score": [1.78979, 0.66081, 0.47959]
    }
).plot(x="test_eval", y="score", figsize=(8, 6)).get_figure()
fig.savefig('model_test_score.png')

Hyperparameter table¶

In [91]:
# The 3 hyperparameters we tuned with the kaggle score as the result
pd.DataFrame({
    "model": ["initial", "add_features", "hpo"],
    "timelimit": ["time_limit = 600", "time_limit=600", "time_limit=600"],
    "presets": ["presets='best_quality'", "presets='best_quality'", "presets='best_quality'"],
    "hp-method": ["none", "problem_type = 'regression'", "nn & GBM"],
    "score": [1.78979, 0.66081, 0.47959]
})
          model         timelimit                 presets  \
0       initial  time_limit = 600  presets='best_quality'   
1  add_features    time_limit=600  presets='best_quality'   
2           hpo    time_limit=600  presets='best_quality'   

                     hp-method    score  
0                         none  1.78979  
1  problem_type = 'regression'  0.66081  
2                     nn & GBM  0.47959